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Traceable Claims in Bioinformatics

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     Quick Analysis Plan



    FibrilNet analysis requires reconstructing a 17,997-node BioGRID graph with 9 GO-semantic edge features, implementing semantic-RWR diffusion across cardiac ATTR (n=151), renal AA (n=48), ALECT2 (n=124), and neurologic ATTRv (n=202) modules, and validating against reported effect sizes (MRR gains up to 0.088, Wilcoxon p values down to 4.46Γ—10⁻²³) using leave-one-out reconstruction with paired tests and bootstrap CIs.


     Long Analysis Plan



    Evidence Base

    FibrilNet is a 17,997-node human protein-protein interaction graph (BioGRID, 925,977 interactions) augmented with 9-dimensional Gene Ontology semantic edge features, applied to reconstruct amyloid disease modules via random walk with restart (RWR) .

    Reported effect sizes across four modules were substantial and statistically robust, with paired one-sided Wilcoxon tests yielding p values spanning 9.49Γ—10⁻⁴ to 4.46Γ—10⁻²³.

    The logarithmic scale is necessary because renal AA semantic-RWR (MRR 0.09061) exceeds the neurologic downregulated baseline by two orders of magnitude. Renal AA showed the largest paired βˆ†MRR (0.0881, 95% bootstrap CI 0.0282–0.1641), while cardiac ATTR had the highest statistical significance (p = 4.46Γ—10⁻²³, 132/151 targets improved, median semantic/topological rank ratio 0.253).

    Computational Pipeline Plan

    The reproducible analysis requires five sequential stages, each with explicit validation criteria drawn from the reported data:

    • Stage 1 β€” Graph construction: Parse BioGRID interactome to 17,997 nodes / 925,977 edges; map all node identifiers to UniProt accession via the Ensembl homology service; annotate each edge with a 9-dimensional GO semantic similarity vector derived from Molecular Function, Biological Process, and Cellular Component ontologies.
    • Stage 2 β€” Module definition: Import cardiac ATTR plaque proteome (151 mapped proteins), kidney AA atlas (48 from 418 AA vs 2,130 comparator, FDR<0.05, positive logβ‚‚ effect), kidney ALECT2 atlas (124 from 474 vs 1,618), and neurologic ATTRv sural-nerve proteome (202 of 213 humanized via one-to-one Ensembl orthologs; stratify 38 upregulated / 164 downregulated).
    • Stage 3 β€” Diffusion: Implement topology-only RWR and semantic-RWR (transition probability weighted by normalized GO semantic edge similarity) with restart parameter calibrated on the 151-protein cardiac ATTR module.
    • Stage 4 β€” Leave-one-out validation: For each held-out target, rank all 17,997 nodes by RWR score; compute MRR, Recall@100, Recall@200, median rank; apply one-sided paired Wilcoxon signed-rank test with Benjamini-Hochberg correction across modules and Holm correction within neurologic direction strata.
    • Stage 5 β€” Cross-disease overlap: Compute Jaccard similarity and feature-enrichment core (FEC) across the four modules; verify APCS/VTN/TIMP3 at FEC=1 and APOE at FEC=0.75.

    Cross-Disease Overlap Structure

    The reported Jaccard similarities define a clear tissue-dominant architecture: cardiac ATTR–AL overlap (0.387) is highest because both derive from cardiac tissue, while all cardiac–renal comparisons (0.067–0.136) and cardiac–neurologic comparison (0.0569) are low. This means semantic-RWR gains are concentrated within-tissue, and cross-tissue convergence is restricted to the APCS/VTN/TIMP3 core.

    Note: Cardiac–renal pairwise values (0.067–0.136 range) are shown as representative midpoints; cardiac–neurologic = 0.0569 is directly reported. Underspecified pairwise values (AL–AA, AL–ALECT2, AL–neurologic, AA–ALECT2, AA–neurologic, ALECT2–neurologic) are not individually reported and are shown as structural placeholders only.

    Known Limitations and Blind Spots

    • The four modules derive from different tissues, species (mouse sural nerve for ATTRv), and experimental designs; effect sizes may not transfer across tissue contexts.
    • Interaction networks are incomplete and literature-biased; GO annotation depth is non-uniform across protein families.
    • The neurologic upregulated subgroup (n=38) showed no significant semantic-RWR gain after Holm correction, indicating the ontology-aware advantage is direction-specific.
    • TTR-seeded recovery remains weak even with semantics (MRR 0.005684), confirming that precursor identity is separable from the distributed downstream environment.
    • No human variant-resolved ATTRv tissue dataset has been analyzed; the proposed precursor-plus-tissue decomposition remains untested in human neurologic biopsies.

    All derived modules, mapping tables, and figure source data are archived at Zenodo (doi:10.5281/zenodo.22161436) for direct reproduction.



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    Updated: October 05, 2026

     Analysis Wizard



    Reconstructing the FibrilNet BioGRID interaction graph, computing GO semantic edge weights, running semantic-RWR diffusion across cardiac ATTR and renal amyloid modules, and validating with leave-one-out MRR against reported effect sizes.



     Hypothesis Graveyard



    Amyloid cross-disease convergence is driven by abundance artifacts (highly abundant plasma proteins appearing in all deposits). The APCS/VTN/TIMP3 core survives enrichment analysis with FDR<0.05 and positive effect requirements, ruling out pure abundance as sufficient explanation.


    Precursor seeding should dominate module recovery because the precursor protein initiates aggregation. TTR-seeded RWR achieved only MRR 0.005684 even with semantics, demonstrating that precursor identity alone is a poor predictor of the distributed downstream environment.

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